In what follows, we do not report the results for the regression-based
approaches. Both the goal-agnostic and the goal-informed regression
approaches perform poorly and can not navigate without collision,
in contrast to [1]} who report success at a
regression approach, as well as [2]} whose method is
goal-informed. We believe differences in training sets may be responsible for
the differences in performance.
Our training scenes involve randomly placed obstacles in the field of
view and simulate only one instance in time, whereas those from
[1]} appear to be mostly corridor images that arise
from actually executing a path. Our learnt model would have no notion of
a corridor in its internal representation. The model in
[2]} is trained in a sparser environment with
large obstacles using navigation commands from a global planner.
Topologically distinct trajectory splits would not be as frequently
observed in those models.
